At the core of the Solr/Lucene search engine is an inverted index. The inverted index has a list of tokens and a list of the documents that contain those tokens. In order to index text, Solr needs to break strings of text into “tokens.” In English and Western European languages spaces are used to separate words, so Solr uses whitespace to determine what is a token for indexing. In a number of languages the words are not separated by spaces. Chinese, Japanese, (the C and J in CJK), Thai, and Vietnamese are common languages that do not use white space to separate words For languages that do not use spaces, a segmentation algorithm has to be used.
We will concentrate on Chinese word segmentation in this post.   Chinese word segmentation is an open problem in the research literature. Native speakers do not necessarily agree on how to segment groups of characters into words (Sproat, Shih, Gale, & Chang, 1994; Wu & Fung, 1994. ) The problem is that a group of characters might be segmented in different ways resulting in different meanings. There are Chinese jokes based on these ambiguities. In the example below from (Teahan, McNab, Wen, & Witten, 2000)the first segmentation means “I like New Zealand Flowers.” The second segmentation means “I like fresh broccoli”
The examples below are from (Wong et. al. 2010, p 38)
才能 as a two character word means "talent"
才 能 as two one character words means "only then able to"
学会 as a two character word means "society"
学 会 as two one character words means "learn how to"
The figure below, Figure 3 from (Teahan et al., 2000) p 376., shows how the 3 character word for physics/physicist interpreted as single characters on the left could result in documents about evidence, barbers, and credit.
Segmentation approaches are generally either character-based or word-based. Segmenting into character unigrams or bigrams is computationally easy and requires no knowledge of the language (So it will work on Chinese, Japanese etc.) Word-based approaches are more accurate in terms of correct segmentation, but are computationally intensive and require dictionaries and/or a large appropriate training corpus. Dealing with ambiguities and out of vocabulary words is a problem with word based approaches (Fu, Kit, & Webster, 2008, Wong et.al 2010.)[ 
Information retrieval research indicates that completely accurate segmentation is not necessary for decent retrieval ( Foo, S., & Li, H. 2004, Huang et.al. 2003, Kwok, 1999 ). In fact Huang et.al.( 2003) found that as segmentation accuracy increased above 70%, information retrieval performance declined. They attribute this to the fact that poor segmentation will tend to break compound words into smaller constituents. Often the smaller consituents of larger words have related meanings so this increases recall. As long as the same segmentation algorithm is used for both segmenting the query and segmenting the text for indexing, good retrieval is possible with character-based approaches. 
Information retrieval research has generally found that simple approaches such as indexing overlapping character bigrams have comparable performance with more sophisticated word based approaches. As an example of overlapping bigrams, if the characters “ABCD” were Chinese characters the tokenizer would split them up into “AB” “BC” and “CD.”
Solr/Lucene implementation issues
Solr/Lucene offers a choice of 5 options for CJK tokenization :
The Solr QueryParser and CJK processing.
The Solr QueryParser had a bug which caused any string that is separated into separate tokens by the filter chain to be searched as a phrase query.  So until this bug was fixed, it didn’t matter which of the Analyzer/Tokenizers you used. Your Analyzer would break a string of CJK characters into unigrams, bigrams or words, but the QueryParser would then put them back together as a phrase. (See: https://issues.apache.org/jira/browse/LUCENE-2458 ).
While this may not seem like a problem for very short queries (1-3 characters,) for longer queries it can be a problem. For example a search for “中国对熊猫的保护”(Protection of Pandas) gets no hits when searched as a phrase in large scale search. However if it is searched as overlapping bigrams there are about 600 hits. 
This bug in the QueryParser is also what caused problems for us with words like “l’art” triggering a phrase search with a common word “l”. (See: http://www.hathitrust.org/blogs/large-scale-search/tuning-search-performance  ). Prior to the Solr bug fix, we tried to get around the problem with a custom punctuation filter that did not split “l’art” into two tokens. However, we subsequently discovered that our custom punctuation filter caused a number of other problems.
The QueryParser bug was fixed with a configuration parameter in Solr 3.1. The fix implemented in Solr 3.1 allows setting autoGeneratePhraseQueries="false" in the fieldType definition for a field in the Solr schema.xml file. Since this parameter is set on a per-field basis and we have multiple languages in the OCR field, this affects both CJK and other languages.  In some Solr implementations this could be a problem depending on how other languages are tokenized. For example with the default Solr filters, “l’art” would get split into “l” and “art” and then would get searched as a Boolean query “l” AND “art” (or a Boolean query "l" OR "art" depending on your default query operator). In our situation this would lead to many false drops where the word “l” appears somewhere in a document (perhaps as someone’s initial) and the word “art” appears somewhere else in the document.
The current large scale search implementation
We discovered numerous problems with our custom “punctuation filter” which we were using to solve the problem with “l’art”. Among other things it was interacting with dirty OCR to create long strings of nonsense which added millions of nonsense “words” to our index. After some discussion with some of the Solr/Lucene committers we decided to use the ICUTokenizer. The ICUTokenizer is designed by a group of multilingual unicode experts to work well with multiple languages.  It is intelligent about when to remove or split on punctuation so it doesn’t split up “l’art” or “can’t” and does split “rain|snow|sleet” which is how tables sometimes show up in the OCR. The ICUTokenizer also provides segmentation for Lao, Myanmar, and Khmer, and segments Chinese and Japanese (Han and Kanji) characters into unigrams.
Unfortunately unigrams produce many false drops (Halpern, 2006. pp 65-66 ; Kwok, 1999, p 170). As an example of false drops in large scale search, a search for 大亚湾 (Daya Bay) produces over 700,000 hits when searched with the default unigram tokenization. If it is searched as overlapping bigrams there are about 5,000 hits and searched as a phrase about 4900. 
What we would really like to do is to use the ICUTokenizer and have C and J tokenized into both unigrams and overlapping bigrams. We need unigrams for single character queries. We need overlapping bigrams in order to overcome the false drops caused by unigrams. Until the code for the ICUTokenizer is modified to produce bigrams, we will be using unigrams. The temporary solution for the issues with unigrams is a list of suggestions to users to put Chinese “words” in quotes separated by spaces. We worked with one of the librarians in the Asia library and they put together a help page for searching HathiTrust: http://www.lib.umich.edu/node/26889. 
Volunteer Java programmers needed found!
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 The Japanese writing system uses four different character sets, Kanji (characters borrowed from Chinese), Hirigana, Katakana, and borrowed Latin alphabet: romanji. For our purposes, Japanese words written in Kanji have similar issues to Chinese. The ICUTokenizer uses a dictionary based tokenizer for Thai . Vietnamese separates syllables with spaces, so the default of splitting on whitespace produces something somewhat analogous to unigrams. Korean separates words with spaces but has issues similar to decompounding in European languages.
  Huang et.al. (2003, p 358) found that as segmentation accuracy increases over 70% accuracy, that retrieval scores decline. They attribute this to the fact that poor segmentation will accidentally break compound words into smaller constituents which increases recall because the smaller consituents of larger words often have related meanings. This also helps to explain why bigram indexing is competitive with more sophisticated segmentation methods and why combining unigrams and bigrams produce better retrieval results. However, what is not exlained in the literature is why this effect is not offset by a decrease in precision which would be expected when the smaller constituents do not have a meaning related to the larger word.
 Foo and Li (2004) did systematic experimentation combining different segmentation methods for query and indexing and found that the best results are when the same segmentation is used for both query and indexing.  At least when using Chinese search engines, users do not use spaces in their Chinese language queries, see examples in from the Sogou query logs in Xu et.al. (2010.) At one time Google was providing OCR with CJK segmented with a proprietary state-of-the-art segmenter. However, because we could not write a Lucene tokenizer that would segment words the same way that Google was segmenting the OCR, we were unable to take advantage of this segmentation. Additionally, HathiTrust receives OCR from works digitized from the Internet Archive and other sources that do not use the Google segmenter.
 It is estimated that 63%-75% of Chinese words in use are two character words and around 5-8% are unigrams, so this partially explains the effectiveness of bigrams(Nie and Ren 1999). Huang et.al. (2003) , Peng et.al. (2002), and Kwok (1999) also explain that breaking a larger word into bigrams acts similarly to decompounding in European languages as constituents of larger words often have related meanings. There are various approaches to combining unigrams and bigrams reported in the literature and it is not immediately apparent which approaches are relatively easy to implement in Solr.
  See "Lucene In Action; Hatcher, Gospodnetić, & McCandless, (2010) Section 4.8.3 "Analyzing Asian Languages" for options 1-4. There is also a recent contribution of a Japanese Morphological analyzer to lucene which has a segmentation mode:https://issues.apache.org/jira/browse/LUCENE-3305  and a segmenter contribution in progress https://issues.apache.org/jira/browse/LUCENE-2522 
 Unfortunately the CJK analyzer segments anything other than ascii into bigrams (For example Hindi/Devenagari or Cyrillic). See Robert Muir’s comment in https://issues.apache.org/jira/browse/LUCENE-2906  (Confirmed as of Solr 3.3)
  The bug in the Solr query parser only affects filter chains that break a token into multiple tokens with different positions. For example the synonym filter which produces multiple tokens with the same position is unaffected by this bug.
 This is query number C25 in the TREC 5 (Text Retrieval Conference) Chinese retrieval track. See http://trec.nist.gov/pubs/trec5/t5_proceedings.html. topics are available at http://trec.nist.gov/data/topics_noneng/topics.CH1-CH28.chinese.english.gz 
 In our case with large scale search this effect is made worse in that we currently index entire documents and can’t currently give documents with query words that occur closer to each other a higher score due to efficiency issues in processing our huge positions indexes. This means that if one character occurs on page one and another on page 300, the match counts the same as if the two characters were adjacent to each other. See http://en.wikipedia.org/wiki/Daya_Bay  for background of Daya Bay.
  Although the Information Retrieval literature is pretty consistent in recommending a combination of unigrams and bigrams, exactly how they can be combined with Solr remains to be determined. If we were indexing small fields we would copy the field containing CJK and index one field as bigrams and one as unigrams and then combine them with dismax to give much more weight to matches in the bigrams.
 The Blacklight project and the VuFind  project would also benefit from this work. The tokenizer they currently use that is part of the SolrMARC  project uses an older Lucene TokenStream API and will probably not be ported to recent versions of Solr/Lucene. See: http://groups.google.com/group/blacklight-development/browse_thread/thread/eac52ff017170664/212e4e46474e96b3?lnk=gst&q=unicodenormalizationfilterfactory#212e4e46474e96b3  http://groups.google.com/group/blacklight-development/browse_thread/thread/2d9e8c5546ed1ac7/db977a4a824bb4f9?lnk=gst&q=implementers+with+script+needs#db977a4a824bb4f9